Design and Optimization of Real‑time Data Lake Tables with Paimon and Flink for Advertising Diagnostics

The article begins by outlining the challenges of traditional offline data warehouses and real‑time processing pipelines in high‑traffic advertising scenarios, emphasizing the need for low latency, reduced resource consumption, and simplified development.

It introduces Paimon as a unified storage engine that integrates tightly with Flink, enabling partial‑update and primary‑key tables to achieve near‑real‑time data synchronization while supporting both streaming and batch queries.

Several lake‑table design patterns are examined, including primary‑key wide tables, separate append tables, and hybrid partitioned tables, each evaluated for partition granularity, merge mechanisms, and write‑path complexity.

Extensive performance experiments compare resource usage, query latency, and development effort across three solution families, revealing that a partial‑update wide table combined with a dimension wide table offers the best balance of query speed and storage efficiency for the advertising diagnostic use case.

The article also details operational considerations such as data back‑fill, handling of aggregation fields, monitoring of small file counts, and primary‑backup deployment strategies to ensure high availability.

Finally, a cost‑benefit analysis shows that the proposed Paimon‑based near‑real‑time pipeline can reduce total infrastructure spend by up to sixfold compared with pure real‑time solutions while meeting the minute‑level freshness requirements of downstream reporting and product services.